Center for Embedded Networked Sensing
Deployment via Iteration and Mobility
1
Schoellhammer ,
2
Poduri , Amarjeet
3
Singh ,
2
Zhang
Tom
Sameera
Bin
1CSL – http://lecs.cs.ucla.edu 2RESL – http://robotics.usc.edu/resl 3EE http://ee.ucla.edu
Sensor Placement with a Limited Sensor Budgets is Difficult
We address placement in several sensing scenarios using mobility and iterative deployment
Sensing using Mobile Robots
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Iterative Placement
Mobile robots can provide complete coverage for large domains.
Traveling and sensing cost demands efficient path planning.
Estimation of the phenomenon can be improved by
– Maximizing information quality: Initial observations are available by
using dense sampling
– Minimizing reconstruction Error: Initial estimation on the environment
using observations from static sensors
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Deployment of Cameras in an outdoor deployment
– performance varies depending on obstacles, lighting
conditions, background contrast, luminance, etc.
– except obstacles, other factors are difficult to model.
• Deploying sensors belowground
– High installation cost
– Soils can take weeks to resettle
Sampling Design: Determine Where to Take Measurements
•Problem: A sensing environment divided into discrete
observation locations ()
•Constraint: Limited budget (B) over the sensing and the
traveling cost (C) for each available mobile robot (Ri, i 2 N).
•Objective: Find a set of paths (Pi), one for each robot, such
that the total utility: field estimation or collected information (I
([ Pi)) over the visited locations is optimized. Formally:
•min pµ (IMSE(P)) subject to C(P)· B – When optimizing
reconstruction accuracy
•maxPiµI([ Pi) subject to C(Pi)· B, 8 i · N – When optimizing
information quality
Path Coverage
•Problem: Cover a path of arbitrary complexity
•Constraints: The environment has several obstacles, and the cameras
used have limited view
•Objective: Minimize the number of cameras while ensuring that every
point in the path is covered
Auditioning
Problem: Accurately predict belowground conditions
Constraints: Meteorological measurements are available, soil and leaf
litter properties are unavailable
Objective: Apply automatic fitting techniques to fit in the presence of
unknown paramters
Empirical Approaches to Sensor Placement: Mobile Sensors and Iterative Deployment
Optimizing reconstruction accuracy
•Use Local Linear Regression to minimize the integrated mean
square error (IMSE)
•The IMSE can be estimated by using the sample density and
Hessian matrix of the scalar field
•Associate with each location in , a reward defined as the decrease
of the IMSE if more sensor readings taken at x.
•Static sensor nodes are deployed uniformly and the readings is used
to estimate the reward for each location.
Data-Driven Approach
•Map obstacles in the environment
•Start with a uniform deployment
•Gather detection data and estimate the camera’s detection range at
different points (and different directions) along the path.
• Solve 1D connected set cover to find optimal camera locations
Camera set-up
Obstacle map
Optimal deployment
Auditioning
Path for Single Robot
A buoy and a robotic boat
Optimizing collected information
•Use mutual information over the set of observed locations as the
optimization criterion
•Provides approximation guarantee of O(logN)
•Provided a simple sequential allocation approach for multiple robots
with strong (near optimal) approximation guarantee:
•If  is the approximation guarantee for single robot path planning
then sequential-allocation provides approximation guarantee of (1+)
•Model belowground temperature from aboveground meteorological
measurements, leveraging phenomenon specific knowledge
•Phenomenon specific models cannot be used directly due to several
unknown physical parameters: soil composition, leaf litter thickness,
leaf litter composition
•Apply multivariate adaptive spline regression to automatically
choose inputs
Robot-3
Robot-1
Robot-2
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